PostEra

AI Research Scientist

• Develop the agentic research vertical at PostEra using agentic systems to automate development of mechanistic models for biochemical and physiological processes
• Analyse biological data for target validation and use models to drive drug discovery decisions
• Develop machine learning methods that adapt to new drug discovery problems from limited labeled data
• Build molecular and tabular in-context learning systems and foundation models from proprietary multimodal data
• Determine relevant prior examples and tasks, quantify when transfer is helpful or harmful, and provide reliable predictions under distribution shift
• Define tasks, construct datasets and evaluation episodes, develop baselines, train and scale models, perform rigorous ablations, and translate successful methods into capabilities used by scientists
• Develop and benchmark agentic systems for quantitative biological and physiological models
• Independently identify, formulate, and lead research projects involving in-context learning, agentic systems, few-shot adaptation, tabular foundation models, and molecular machine learning
• Design and train models adapting to new assays, endpoints, targets, or chemical series
• Rigorously compare approaches against strong baselines and curate bias-sensitive test cases
• Collaborate with scientists on potency modeling, ADME prediction, selectivity, lead optimization, and early clinical study design
• Develop efficient training and data pipelines and scale models across molecular and tabular tasks
• Produce readable, reproducible research code; track experiments; contribute to code review, documentation, and shared modeling infrastructure
• Publish results in leading machine learning, medicinal chemistry, or computational biology venues and represent PostEra to the scientific community

Machine Learning Researcher – Agentic Science